library("FRESA.CAD")
library(readxl)
library(igraph)
library(umap)
library(tsne)
library(entropy)
op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('table.split.table', 400)
pander::panderOptions('keep.trailing.zeros',TRUE)
dataGI <- as.data.frame(read_excel("~/GitHub/LatentBiomarkers/Data/GI/data.xlsx", sheet = "Sheet1"))
dataGI$ID <- NULL
table(dataGI$V2)
#>
#> 1 2
#> 76 76
dataSet1 <- subset(dataGI,V2==1)
class <- dataSet1$V1
dataSet1$V1 <- NULL
dataSet1$V2 <- NULL
colnames(dataSet1) <- paste(colnames(dataSet1),"WL",sep="_")
dataSet2 <- subset(dataGI,V2==2)
dataSet2$V1 <- NULL
dataSet2$V2 <- NULL
colnames(dataSet2) <- paste(colnames(dataSet2),"NBI",sep="_")
dataGI <- cbind(dataSet1,dataSet2)
dataGI$class <- 1*(class > 1)
table(dataGI$class)
#>
#> 0 1
#> 21 55
studyName <- "GI"
dataframe <- dataGI
outcome <- "class"
TopVariables <- 10
thro <- 0.80
cexheat = 0.15
Some libraries
library(psych)
library(whitening)
library("vioplot")
library("rpart")
pander::pander(c(rows=nrow(dataframe),col=ncol(dataframe)-1))
| rows | col |
|---|---|
| 76 | 1396 |
pander::pander(table(dataframe[,outcome]))
| 0 | 1 |
|---|---|
| 21 | 55 |
varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]
largeSet <- length(varlist) > 1500
Scaling and removing near zero variance columns and highly co-linear(r>0.99999) columns
### Some global cleaning
sdiszero <- apply(dataframe,2,sd) > 1.0e-16
dataframe <- dataframe[,sdiszero]
varlist <- colnames(dataframe)[colnames(dataframe) != outcome]
tokeep <- c(as.character(correlated_Remove(dataframe,varlist,thr=0.99999)),outcome)
dataframe <- dataframe[,tokeep]
varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]
iscontinous <- sapply(apply(dataframe,2,unique),length) > 5 ## Only variables with enough samples
dataframeScaled <- FRESAScale(dataframe,method="OrderLogit")$scaledData
numsub <- nrow(dataframe)
if (numsub > 1000) numsub <- 1000
if (!largeSet)
{
hm <- heatMaps(data=dataframeScaled[1:numsub,],
Outcome=outcome,
Scale=TRUE,
hCluster = "row",
xlab="Feature",
ylab="Sample",
srtCol=45,
srtRow=45,
cexCol=cexheat,
cexRow=cexheat
)
par(op)
}
The heat map of the data
if (!largeSet)
{
par(cex=0.6,cex.main=0.85,cex.axis=0.7)
#cormat <- Rfast::cora(as.matrix(dataframe[,varlist]),large=TRUE)
cormat <- cor(dataframe[,varlist],method="pearson")
cormat[is.na(cormat)] <- 0
gplots::heatmap.2(abs(cormat),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Original Correlation",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Pearson Correlation|",
xlab="Feature", ylab="Feature")
diag(cormat) <- 0
print(max(abs(cormat)))
}
[1]
0.9999797
DEdataframe <- IDeA(dataframe,verbose=TRUE,thr=thro)
#>
#> Included: 725 , Uni p: 0.01309165 , Uncorrelated Base: 29 , Outcome-Driven Size: 0 , Base Size: 29
#>
#>
1 <R=1.000,r=0.975,N= 185>, Top: 31( 29 )[ 1 : 31 Fa= 30 : 0.975 ]( 30 , 78 , 0 ),<|>Tot Used: 108 , Added: 78 , Zero Std: 0 , Max Cor: 1.000
#>
2 <R=1.000,r=0.975,N= 185>, Top: 8( 26 )[ 1 : 8 Fa= 38 : 0.975 ]( 8 , 51 , 30 ),<|>Tot Used: 153 , Added: 51 , Zero Std: 0 , Max Cor: 1.000
#>
3 <R=1.000,r=0.975,N= 185>, Top: 7( 15 )[ 1 : 7 Fa= 44 : 0.975 ]( 7 , 30 , 38 ),<|>Tot Used: 178 , Added: 30 , Zero Std: 0 , Max Cor: 0.999
#>
4 <R=0.999,r=0.975,N= 185>, Top: 5( 6 )[ 1 : 5 Fa= 48 : 0.975 ]( 4 , 24 , 44 ),<|>Tot Used: 181 , Added: 24 , Zero Std: 0 , Max Cor: 0.975
#>
5 <R=0.975,r=0.962,N= 185>, Top: 53( 1 )[ 1 : 53 Fa= 80 : 0.962 ]( 51 , 59 , 48 ),<|>Tot Used: 247 , Added: 59 , Zero Std: 0 , Max Cor: 0.974
#>
6 <R=0.974,r=0.962,N= 185>, Top: 4( 1 )[ 1 : 4 Fa= 83 : 0.962 ]( 4 , 4 , 80 ),<|>Tot Used: 251 , Added: 4 , Zero Std: 0 , Max Cor: 0.962
#>
7 <R=0.962,r=0.931,N= 222>, Top: 75[ 3 ]( 1 )=[ 2 : 75 Fa= 127 : 0.942 ]( 72 , 105 , 83 ),<|>Tot Used: 368 , Added: 105 , Zero Std: 0 , Max Cor: 0.994
#>
8 <R=0.994,r=0.947,N= 222>, Top: 9( 1 )[ 1 : 9 Fa= 135 : 0.947 ]( 9 , 9 , 127 ),<|>Tot Used: 383 , Added: 9 , Zero Std: 0 , Max Cor: 0.941
#>
9 <R=0.941,r=0.871,N= 302>, Top: 93( 1 )=[ 2 : 93 Fa= 182 : 0.911 ]( 87 , 139 , 135 ),<|>Tot Used: 523 , Added: 139 , Zero Std: 0 , Max Cor: 0.960
#>
10 <R=0.960,r=0.880,N= 302>, Top: 20( 1 )[ 1 : 20 Fa= 191 : 0.880 ]( 18 , 26 , 182 ),<|>Tot Used: 545 , Added: 26 , Zero Std: 0 , Max Cor: 0.915
#>
11 <R=0.915,r=0.807,N= 245>, Top: 73( 5 )[ 1 : 73 Fa= 219 : 0.807 ]( 71 , 121 , 191 ),<|>Tot Used: 593 , Added: 121 , Zero Std: 0 , Max Cor: 0.926
#>
12 <R=0.926,r=0.813,N= 245>, Top: 17( 3 )[ 1 : 17 Fa= 226 : 0.813 ]( 16 , 20 , 219 ),<|>Tot Used: 610 , Added: 20 , Zero Std: 0 , Max Cor: 0.851
#>
13 <R=0.851,r=0.800,N= 49>, Top: 23( 1 )[ 1 : 23 Fa= 234 : 0.800 ]( 23 , 26 , 226 ),<|>Tot Used: 626 , Added: 26 , Zero Std: 0 , Max Cor: 0.799
#>
14 <R=0.799,r=0.800,N= 49>
#>
[ 14 ], 0.7986822 Decor Dimension: 626 Nused: 626 . Cor to Base: 244 , ABase: 17 , Outcome Base: 0
#>
varlistc <- colnames(DEdataframe)[colnames(DEdataframe) != outcome]
pander::pander(sum(apply(dataframe[,varlist],2,var)))
7.73e+08
pander::pander(sum(apply(DEdataframe[,varlistc],2,var)))
1.8e+08
pander::pander(entropy(discretize(unlist(dataframe[,varlist]), 256)))
0.306
pander::pander(entropy(discretize(unlist(DEdataframe[,varlistc]), 256)))
0.218
if (!largeSet)
{
par(cex=0.6,cex.main=0.85,cex.axis=0.7)
UPSTM <- attr(DEdataframe,"UPSTM")
gplots::heatmap.2(1.0*(abs(UPSTM)>0),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Decorrelation matrix",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Beta|>0",
xlab="Output Feature", ylab="Input Feature")
par(op)
}
if (!largeSet)
{
cormat <- cor(DEdataframe[,varlistc],method="pearson")
cormat[is.na(cormat)] <- 0
gplots::heatmap.2(abs(cormat),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Correlation after IDeA",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Pearson Correlation|",
xlab="Feature", ylab="Feature")
par(op)
diag(cormat) <- 0
print(max(abs(cormat)))
}
[1]
0.9419448
if (nrow(dataframe) < 1000)
{
classes <- unique(dataframe[1:numsub,outcome])
raincolors <- rainbow(length(classes))
names(raincolors) <- classes
datasetframe.umap = umap(scale(dataframe[1:numsub,varlist]),n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: Original",t='n')
text(datasetframe.umap$layout,labels=dataframe[1:numsub,outcome],col=raincolors[dataframe[1:numsub,outcome]+1])
}
if (nrow(dataframe) < 1000)
{
datasetframe.umap = umap(scale(DEdataframe[1:numsub,varlistc]),n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: After IDeA",t='n')
text(datasetframe.umap$layout,labels=DEdataframe[1:numsub,outcome],col=raincolors[DEdataframe[1:numsub,outcome]+1])
}
univarRAW <- uniRankVar(varlist,
paste(outcome,"~1"),
outcome,
dataframe,
rankingTest="AUC")
100 : V102_WL 200 : V288_WL 300 : V535_WL 400 : V635_WL 500 :
V37_NBI
600 : V137_NBI 700 : V470_NBI
univarDe <- uniRankVar(varlistc,
paste(outcome,"~1"),
outcome,
DEdataframe,
rankingTest="AUC",
)
100 : La_V102_WL 200 : La_V288_WL 300 : La_V535_WL 400 : La_V635_WL
500 : La_V37_NBI
600 : La_V137_NBI 700 : La_V470_NBI
univariate_columns <- c("caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC")
##topfive
topvar <- c(1:length(varlist)) <= TopVariables
pander::pander(univarRAW$orderframe[topvar,univariate_columns])
| caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | |
|---|---|---|---|---|---|---|
| V172_WL | 3.55e+03 | 1.78e+03 | 1046.667 | 537.2409 | 0.718095 | 0.933 |
| V220_NBI | 2.01e+02 | 1.20e+02 | 51.524 | 27.8220 | 0.747592 | 0.929 |
| V220_WL | 1.96e+02 | 1.07e+02 | 52.381 | 42.7370 | 0.097268 | 0.927 |
| V477_NBI | 6.18e-02 | 2.98e-02 | 0.149 | 0.1717 | 0.000358 | 0.925 |
| V169_NBI | 1.26e+03 | 8.24e+02 | 346.619 | 198.5476 | 0.350000 | 0.920 |
| V196_NBI | 4.52e+02 | 2.51e+02 | 134.238 | 66.3226 | 0.410564 | 0.920 |
| V182_NBI | 3.44e+02 | 2.17e+02 | 95.190 | 48.8412 | 0.793090 | 0.915 |
| V470_NBI | 3.79e-01 | 1.34e-01 | 0.188 | 0.0682 | 0.948083 | 0.913 |
| V182_WL | 3.17e+02 | 1.69e+02 | 96.476 | 87.3691 | 0.142781 | 0.912 |
| V474_NBI | 3.40e+00 | 3.13e-01 | 2.680 | 0.5481 | 0.222068 | 0.912 |
topLAvar <- univarDe$orderframe$Name[str_detect(univarDe$orderframe$Name,"La_")]
topLAvar <- unique(c(univarDe$orderframe$Name[topvar],topLAvar[1:as.integer(TopVariables/2)]))
finalTable <- univarDe$orderframe[topLAvar,univariate_columns]
theLaVar <- rownames(finalTable)[str_detect(rownames(finalTable),"La_")]
pander::pander(univarDe$orderframe[topLAvar,univariate_columns])
| caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | |
|---|---|---|---|---|---|---|
| V474_NBI | 3.40e+00 | 3.13e-01 | 2.68e+00 | 5.48e-01 | 0.2221 | 0.912 |
| V169_WL | 1.20e+03 | 6.66e+02 | 4.03e+02 | 4.20e+02 | 0.0543 | 0.897 |
| V474_WL | 3.19e+00 | 4.57e-01 | 2.36e+00 | 5.29e-01 | 0.9972 | 0.882 |
| V4_WL | 1.67e+03 | 9.90e+02 | 6.00e+02 | 4.77e+02 | 0.0868 | 0.874 |
| La_V69_WL | 1.03e-03 | 1.66e-03 | -1.02e-03 | 1.56e-03 | 0.3074 | 0.872 |
| V473_NBI | 1.22e-01 | 4.19e-02 | 2.12e-01 | 1.67e-01 | 0.0188 | 0.865 |
| V485_WL | 3.14e+00 | 4.63e-01 | 2.44e+00 | 4.51e-01 | 0.5556 | 0.853 |
| V473_WL | 1.57e-01 | 5.21e-02 | 2.82e-01 | 1.38e-01 | 0.3081 | 0.850 |
| V198_NBI | 3.83e+02 | 2.17e+02 | 1.54e+02 | 9.18e+01 | 0.3333 | 0.844 |
| La_V200_NBI | -1.08e+03 | 2.09e+03 | 1.03e+03 | 2.22e+03 | 0.0393 | 0.835 |
| La_V91_NBI | 6.01e-03 | 4.07e-03 | 1.88e-03 | 2.59e-03 | 0.9531 | 0.825 |
| La_V478_WL | 9.13e-01 | 2.30e-02 | 9.48e-01 | 3.09e-02 | 0.9838 | 0.824 |
| La_V296_NBI | -3.21e+02 | 1.47e+03 | 3.76e+02 | 9.94e+02 | 0.0346 | 0.810 |
dc <- getLatentCoefficients(DEdataframe)
fscores <- attr(DEdataframe,"fscore")
theSigDc <- dc[theLaVar]
names(theSigDc) <- NULL
theSigDc <- unique(names(unlist(theSigDc)))
theFormulas <- dc[rownames(finalTable)]
deFromula <- character(length(theFormulas))
names(deFromula) <- rownames(finalTable)
pander::pander(c(mean=mean(sapply(dc,length)),total=length(dc),fraction=length(dc)/(ncol(dataframe)-1)))
| mean | total | fraction |
|---|---|---|
| 2.35 | 546 | 0.748 |
allSigvars <- names(dc)
dx <- names(deFromula)[1]
for (dx in names(deFromula))
{
coef <- theFormulas[[dx]]
cname <- names(theFormulas[[dx]])
names(cname) <- cname
for (cf in names(coef))
{
if (cf != dx)
{
if (coef[cf]>0)
{
deFromula[dx] <- paste(deFromula[dx],
sprintf("+ %5.3f*%s",coef[cf],cname[cf]))
}
else
{
deFromula[dx] <- paste(deFromula[dx],
sprintf("%5.3f*%s",coef[cf],cname[cf]))
}
}
}
}
finalTable <- rbind(finalTable,univarRAW$orderframe[theSigDc[!(theSigDc %in% rownames(finalTable))],univariate_columns])
orgnamez <- rownames(finalTable)
orgnamez <- str_remove_all(orgnamez,"La_")
finalTable$RAWAUC <- univarRAW$orderframe[orgnamez,"ROCAUC"]
finalTable$DecorFormula <- deFromula[rownames(finalTable)]
finalTable$fscores <- fscores[rownames(finalTable)]
Final_Columns <- c("DecorFormula","caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC","RAWAUC","fscores")
finalTable <- finalTable[order(-finalTable$ROCAUC),]
pander::pander(finalTable[,Final_Columns])
| DecorFormula | caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | RAWAUC | fscores | |
|---|---|---|---|---|---|---|---|---|---|
| V474_NBI | 3.40e+00 | 3.13e-01 | 2.68e+00 | 5.48e-01 | 0.2221 | 0.912 | 0.912 | NA | |
| V169_WL | 1.20e+03 | 6.66e+02 | 4.03e+02 | 4.20e+02 | 0.0543 | 0.897 | 0.897 | 9 | |
| V474_WL | 3.19e+00 | 4.57e-01 | 2.36e+00 | 5.29e-01 | 0.9972 | 0.882 | 0.882 | NA | |
| V4_WL | 1.67e+03 | 9.90e+02 | 6.00e+02 | 4.77e+02 | 0.0868 | 0.874 | 0.874 | 4 | |
| La_V69_WL | -1.863V47_WL + 1.000V69_WL | 1.03e-03 | 1.66e-03 | -1.02e-03 | 1.56e-03 | 0.3074 | 0.872 | 0.617 | -1 |
| V473_NBI | 1.22e-01 | 4.19e-02 | 2.12e-01 | 1.67e-01 | 0.0188 | 0.865 | 0.865 | 2 | |
| V485_WL | 3.14e+00 | 4.63e-01 | 2.44e+00 | 4.51e-01 | 0.5556 | 0.853 | 0.853 | 1 | |
| V473_WL | 1.57e-01 | 5.21e-02 | 2.82e-01 | 1.38e-01 | 0.3081 | 0.850 | 0.850 | 1 | |
| V198_NBI | 3.83e+02 | 2.17e+02 | 1.54e+02 | 9.18e+01 | 0.3333 | 0.844 | 0.844 | 13 | |
| La_V200_NBI | -0.862V184_NBI + 1.000V200_NBI | -1.08e+03 | 2.09e+03 | 1.03e+03 | 2.22e+03 | 0.0393 | 0.835 | 0.763 | -1 |
| La_V91_NBI | -2.866V47_NBI + 1.000V91_NBI | 6.01e-03 | 4.07e-03 | 1.88e-03 | 2.59e-03 | 0.9531 | 0.825 | 0.578 | 1 |
| La_V478_WL | + 0.130V475_WL + 1.000V478_WL | 9.13e-01 | 2.30e-02 | 9.48e-01 | 3.09e-02 | 0.9838 | 0.824 | 0.788 | 1 |
| V184_NBI | NA | 1.75e+04 | 1.38e+04 | 7.48e+03 | 6.94e+03 | 0.0334 | 0.815 | 0.815 | NA |
| La_V296_NBI | -13.104V198_NBI + 1.000V296_NBI | -3.21e+02 | 1.47e+03 | 3.76e+02 | 9.94e+02 | 0.0346 | 0.810 | 0.758 | -1 |
| V478_WL | NA | 8.40e-01 | 5.41e-02 | 8.90e-01 | 6.37e-02 | 0.4143 | 0.788 | 0.788 | NA |
| V200_NBI | NA | 1.41e+04 | 1.22e+04 | 7.48e+03 | 7.85e+03 | 0.0148 | 0.763 | 0.763 | NA |
| V296_NBI | NA | 4.70e+03 | 3.29e+03 | 2.40e+03 | 2.04e+03 | 0.1764 | 0.758 | 0.758 | NA |
| V475_WL | NA | 5.66e-01 | 3.42e-01 | 4.48e-01 | 5.50e-01 | 0.0297 | 0.730 | 0.730 | 3 |
| V69_WL | NA | 1.58e-02 | 5.93e-03 | 1.34e-02 | 5.45e-03 | 0.7833 | 0.617 | 0.617 | NA |
| V91_NBI | NA | 2.49e-02 | 7.61e-03 | 2.23e-02 | 8.15e-03 | 0.9484 | 0.578 | 0.578 | NA |
| V47_NBI | NA | 6.61e-03 | 2.17e-03 | 7.13e-03 | 2.65e-03 | 0.6695 | 0.558 | 0.558 | 6 |
| V47_WL | NA | 7.93e-03 | 3.09e-03 | 7.72e-03 | 2.76e-03 | 0.9876 | 0.492 | 0.492 | NA |
featuresnames <- colnames(dataframe)[colnames(dataframe) != outcome]
pc <- prcomp(dataframe[,iscontinous],center = TRUE,tol=0.002) #principal components
predPCA <- predict(pc,dataframe[,iscontinous])
PCAdataframe <- as.data.frame(cbind(predPCA,dataframe[,!iscontinous]))
colnames(PCAdataframe) <- c(colnames(predPCA),colnames(dataframe)[!iscontinous])
#plot(PCAdataframe[,colnames(PCAdataframe)!=outcome],col=dataframe[,outcome],cex=0.65,cex.lab=0.5,cex.axis=0.75,cex.sub=0.5,cex.main=0.75)
#pander::pander(pc$rotation)
PCACor <- cor(PCAdataframe[,colnames(PCAdataframe) != outcome])
gplots::heatmap.2(abs(PCACor),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "PCA Correlation",
cexRow = 0.5,
cexCol = 0.5,
srtCol=45,
srtRow= -45,
key.title=NA,
key.xlab="Pearson Correlation",
xlab="Feature", ylab="Feature")
EFAdataframe <- dataframeScaled
if (length(iscontinous) < 2000)
{
topred <- min(length(iscontinous),nrow(dataframeScaled),ncol(predPCA)/2)
if (topred < 2) topred <- 2
uls <- fa(dataframeScaled[,iscontinous],nfactors=topred,rotate="varimax",warnings=FALSE) # EFA analysis
predEFA <- predict(uls,dataframeScaled[,iscontinous])
EFAdataframe <- as.data.frame(cbind(predEFA,dataframeScaled[,!iscontinous]))
colnames(EFAdataframe) <- c(colnames(predEFA),colnames(dataframeScaled)[!iscontinous])
EFACor <- cor(EFAdataframe[,colnames(EFAdataframe) != outcome])
gplots::heatmap.2(abs(EFACor),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "EFA Correlation",
cexRow = 0.5,
cexCol = 0.5,
srtCol=45,
srtRow= -45,
key.title=NA,
key.xlab="Pearson Correlation",
xlab="Feature", ylab="Feature")
}
par(op)
par(xpd = TRUE)
dataframe[,outcome] <- factor(dataframe[,outcome])
rawmodel <- rpart(paste(outcome,"~."),dataframe,control=rpart.control(maxdepth=3))
pr <- predict(rawmodel,dataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(rawmodel,main="Raw",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(rawmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,dataframe[,outcome]==0))
}
pander::pander(table(dataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 17 | 4 |
| 1 | 3 | 52 |
pander::pander(ptab)
detail:
| statistic | est | lower | upper |
|---|---|---|---|
| ap | 0.7368 | 0.6232 | 0.831 |
| tp | 0.7237 | 0.6091 | 0.820 |
| se | 0.9455 | 0.8488 | 0.989 |
| sp | 0.8095 | 0.5809 | 0.946 |
| diag.ac | 0.9079 | 0.8194 | 0.962 |
| diag.or | 73.6667 | 14.9632 | 362.674 |
| nndx | 1.3245 | 1.0705 | 2.327 |
| youden | 0.7550 | 0.4297 | 0.934 |
| pv.pos | 0.9286 | 0.8271 | 0.980 |
| pv.neg | 0.8500 | 0.6211 | 0.968 |
| lr.pos | 4.9636 | 2.0506 | 12.015 |
| lr.neg | 0.0674 | 0.0220 | 0.206 |
| p.rout | 0.2632 | 0.1687 | 0.377 |
| p.rin | 0.7368 | 0.6232 | 0.831 |
| p.tpdn | 0.1905 | 0.0545 | 0.419 |
| p.tndp | 0.0545 | 0.0114 | 0.151 |
| p.dntp | 0.0714 | 0.0198 | 0.173 |
| p.dptn | 0.1500 | 0.0321 | 0.379 |
tab:
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 52 | 4 | 56 |
| Test - | 3 | 17 | 20 |
| Total | 55 | 21 | 76 |
method: exact
digits: 2
conf.level: 0.95
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.908 | 0.819 | 0.962 |
| 3 | se | 0.945 | 0.849 | 0.989 |
| 4 | sp | 0.810 | 0.581 | 0.946 |
| 6 | diag.or | 73.667 | 14.963 | 362.674 |
par(op)
par(xpd = TRUE)
DEdataframe[,outcome] <- factor(DEdataframe[,outcome])
IDeAmodel <- rpart(paste(outcome,"~."),DEdataframe,control=rpart.control(maxdepth=3))
pr <- predict(IDeAmodel,DEdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(IDeAmodel,main="IDeA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(IDeAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,DEdataframe[,outcome]==0))
}
pander::pander(table(DEdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 18 | 3 |
| 1 | 1 | 54 |
pander::pander(ptab)
detail:
| statistic | est | lower | upper |
|---|---|---|---|
| ap | 0.7500 | 0.63744 | 8.42e-01 |
| tp | 0.7237 | 0.60914 | 8.20e-01 |
| se | 0.9818 | 0.90281 | 1.00e+00 |
| sp | 0.8571 | 0.63658 | 9.70e-01 |
| diag.ac | 0.9474 | 0.87069 | 9.85e-01 |
| diag.or | 324.0000 | 31.67589 | 3.31e+03 |
| nndx | 1.1920 | 1.03194 | 1.85e+00 |
| youden | 0.8390 | 0.53938 | 9.69e-01 |
| pv.pos | 0.9474 | 0.85380 | 9.89e-01 |
| pv.neg | 0.9474 | 0.73972 | 9.99e-01 |
| lr.pos | 6.8727 | 2.40921 | 1.96e+01 |
| lr.neg | 0.0212 | 0.00302 | 1.49e-01 |
| p.rout | 0.2500 | 0.15772 | 3.63e-01 |
| p.rin | 0.7500 | 0.63744 | 8.42e-01 |
| p.tpdn | 0.1429 | 0.03049 | 3.63e-01 |
| p.tndp | 0.0182 | 0.00046 | 9.72e-02 |
| p.dntp | 0.0526 | 0.01099 | 1.46e-01 |
| p.dptn | 0.0526 | 0.00133 | 2.60e-01 |
tab:
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 54 | 3 | 57 |
| Test - | 1 | 18 | 19 |
| Total | 55 | 21 | 76 |
method: exact
digits: 2
conf.level: 0.95
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.947 | 0.871 | 0.985 |
| 3 | se | 0.982 | 0.903 | 1.000 |
| 4 | sp | 0.857 | 0.637 | 0.970 |
| 6 | diag.or | 324.000 | 31.676 | 3314.066 |
par(op)
par(xpd = TRUE)
PCAdataframe[,outcome] <- factor(PCAdataframe[,outcome])
PCAmodel <- rpart(paste(outcome,"~."),PCAdataframe,control=rpart.control(maxdepth=3))
pr <- predict(PCAmodel,PCAdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(PCAmodel,main="PCA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(PCAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,PCAdataframe[,outcome]==0))
}
pander::pander(table(PCAdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 18 | 3 |
| 1 | 6 | 49 |
pander::pander(ptab)
detail:
| statistic | est | lower | upper |
|---|---|---|---|
| ap | 0.6842 | 0.5675 | 0.786 |
| tp | 0.7237 | 0.6091 | 0.820 |
| se | 0.8909 | 0.7775 | 0.959 |
| sp | 0.8571 | 0.6366 | 0.970 |
| diag.ac | 0.8816 | 0.7871 | 0.944 |
| diag.or | 49.0000 | 11.0713 | 216.866 |
| nndx | 1.3368 | 1.0771 | 2.415 |
| youden | 0.7481 | 0.4141 | 0.928 |
| pv.pos | 0.9423 | 0.8405 | 0.988 |
| pv.neg | 0.7500 | 0.5329 | 0.902 |
| lr.pos | 6.2364 | 2.1786 | 17.852 |
| lr.neg | 0.1273 | 0.0586 | 0.276 |
| p.rout | 0.3158 | 0.2139 | 0.433 |
| p.rin | 0.6842 | 0.5675 | 0.786 |
| p.tpdn | 0.1429 | 0.0305 | 0.363 |
| p.tndp | 0.1091 | 0.0411 | 0.222 |
| p.dntp | 0.0577 | 0.0121 | 0.159 |
| p.dptn | 0.2500 | 0.0977 | 0.467 |
tab:
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 49 | 3 | 52 |
| Test - | 6 | 18 | 24 |
| Total | 55 | 21 | 76 |
method: exact
digits: 2
conf.level: 0.95
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.882 | 0.787 | 0.944 |
| 3 | se | 0.891 | 0.778 | 0.959 |
| 4 | sp | 0.857 | 0.637 | 0.970 |
| 6 | diag.or | 49.000 | 11.071 | 216.866 |
par(op)
EFAdataframe[,outcome] <- factor(EFAdataframe[,outcome])
EFAmodel <- rpart(paste(outcome,"~."),EFAdataframe,control=rpart.control(maxdepth=3))
pr <- predict(EFAmodel,EFAdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(EFAmodel,main="EFA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(EFAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,EFAdataframe[,outcome]==0))
}
pander::pander(table(EFAdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 19 | 2 |
| 1 | 4 | 51 |
pander::pander(ptab)
detail:
| statistic | est | lower | upper |
|---|---|---|---|
| ap | 0.6974 | 0.5813 | 0.798 |
| tp | 0.7237 | 0.6091 | 0.820 |
| se | 0.9273 | 0.8241 | 0.980 |
| sp | 0.9048 | 0.6962 | 0.988 |
| diag.ac | 0.9211 | 0.8360 | 0.970 |
| diag.or | 121.1250 | 20.4827 | 716.278 |
| nndx | 1.2019 | 1.0330 | 1.922 |
| youden | 0.8320 | 0.5204 | 0.968 |
| pv.pos | 0.9623 | 0.8702 | 0.995 |
| pv.neg | 0.8261 | 0.6122 | 0.950 |
| lr.pos | 9.7364 | 2.6001 | 36.459 |
| lr.neg | 0.0804 | 0.0310 | 0.209 |
| p.rout | 0.3026 | 0.2025 | 0.419 |
| p.rin | 0.6974 | 0.5813 | 0.798 |
| p.tpdn | 0.0952 | 0.0117 | 0.304 |
| p.tndp | 0.0727 | 0.0202 | 0.176 |
| p.dntp | 0.0377 | 0.0046 | 0.130 |
| p.dptn | 0.1739 | 0.0495 | 0.388 |
tab:
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 51 | 2 | 53 |
| Test - | 4 | 19 | 23 |
| Total | 55 | 21 | 76 |
method: exact
digits: 2
conf.level: 0.95
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.921 | 0.836 | 0.970 |
| 3 | se | 0.927 | 0.824 | 0.980 |
| 4 | sp | 0.905 | 0.696 | 0.988 |
| 6 | diag.or | 121.125 | 20.483 | 716.278 |
par(op)